mirror of https://github.com/alibaba/EasyCV.git
86 lines
3.3 KiB
Python
86 lines
3.3 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
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import os
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import tempfile
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import unittest
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import cv2
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import numpy as np
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from modelscope.outputs import OutputKeys
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from modelscope.pipelines import pipeline
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from modelscope.utils.constant import Tasks
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from modelscope.utils.cv.image_utils import semantic_seg_masks_to_image
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from modelscope.utils.test_utils import test_level
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from PIL import Image
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from tests.ut_config import BASE_LOCAL_PATH
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class EasyCVSegmentationPipelineTest(unittest.TestCase):
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img_path = os.path.join(BASE_LOCAL_PATH,
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'data/test_images/image_segmentation.jpg')
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def setUp(self) -> None:
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self.task = Tasks.image_segmentation
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self.model_id = 'damo/cv_segformer-b0_image_semantic-segmentation_coco-stuff164k'
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def _internal_test_(self, model_id):
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semantic_seg = pipeline(task=Tasks.image_segmentation, model=model_id)
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outputs = semantic_seg(self.img_path)
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draw_img = semantic_seg_masks_to_image(outputs[OutputKeys.MASKS])
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with tempfile.NamedTemporaryFile(suffix='.jpg') as tmp_file:
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tmp_save_path = tmp_file.name
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cv2.imwrite(tmp_save_path, draw_img)
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print('test ' + model_id + ' DONE')
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def _internal_test_batch_(self, model_id, num_samples=2, batch_size=2):
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# TODO: support in the future
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img = np.asarray(Image.open(self.img_path))
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num_samples = num_samples
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batch_size = batch_size
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semantic_seg = pipeline(
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task=Tasks.image_segmentation,
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model=model_id,
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batch_size=batch_size)
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outputs = semantic_seg([self.img_path] * num_samples)
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self.assertEqual(semantic_seg.predict_op.batch_size, batch_size)
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self.assertEqual(len(outputs), num_samples)
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for output in outputs:
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self.assertListEqual(
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list(img.shape)[:2], list(output['seg_pred'].shape))
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@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
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def test_segformer_b0(self):
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model_id = 'damo/cv_segformer-b0_image_semantic-segmentation_coco-stuff164k'
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self._internal_test_(model_id)
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@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
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def test_segformer_b1(self):
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model_id = 'damo/cv_segformer-b1_image_semantic-segmentation_coco-stuff164k'
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self._internal_test_(model_id)
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@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
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def test_segformer_b2(self):
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model_id = 'damo/cv_segformer-b2_image_semantic-segmentation_coco-stuff164k'
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self._internal_test_(model_id)
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@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
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def test_segformer_b3(self):
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model_id = 'damo/cv_segformer-b3_image_semantic-segmentation_coco-stuff164k'
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self._internal_test_(model_id)
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@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
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def test_segformer_b4(self):
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model_id = 'damo/cv_segformer-b4_image_semantic-segmentation_coco-stuff164k'
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self._internal_test_(model_id)
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@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
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def test_segformer_b5(self):
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model_id = 'damo/cv_segformer-b5_image_semantic-segmentation_coco-stuff164k'
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self._internal_test_(model_id)
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if __name__ == '__main__':
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unittest.main()
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